Lossless data hiding for VQ indices based on neighboring correlation

Data hiding is one of the protective techniques for authentication or secret communication through a public and open channel such as the Internet. In this study, a novel lossless data hiding by embedding secret data into a Vector Quantization (VQ)-compressed image is proposed to achieve secret communication and data compression simultaneously. The correlation of neighboring blocks of a VQ-compressed image is explored. It is shown that the neighboring blocks of a VQ-compressed image normally have high mutual correlation. Thus, this scheme employs the neighboring processed compression indices to generate specific sub-codebooks required for encoding and hiding data simultaneously. Since the sizes of sub-codebooks are smaller than that of the original VQ codebook, the encoded size of each index can be significantly reduced. As a result, a great deal of extra free space can be created. Moreover, the original VQ-compressed images can be perfectly recovered after data extraction. To evaluate the effectiveness of this approach, various test images are employed in the experiments. As documented in the experimental results, it is shown that the performance of the proposed scheme is superior to the former schemes in terms of compression ratio, embedding rate, execution time, and embedding capacity.